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Coding > JavaScript > Flashcards

JavaScript Flashcards

1
Q

What is Computational Thinking

A

To think like a computer

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2
Q

What is Computational Thinking used for?

A

Using the specific and unique capabilities of computers to solve problems

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3
Q

What is the main limitation of computers?

A

That they only understand 0’s and 1’s.

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4
Q

How can I begin to speak to a computer in terms it can understand?

A

Simplify my thinking

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5
Q

What are the 4 principles of Computational Thinking?

A
  1. Decomposition
  2. Abstraction
  3. Pattern Recognition
  4. Algorithm Writing
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6
Q

What is Decomposition?

A

Breaking a BIG problem down into the many smaller problems involved

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7
Q

How is a BIG problem solved?

A

By solving the smaller problems involved

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8
Q

What is Pattern Recognition?

A
  1. Looking for commonalities amongst the smaller problems

2. Looking for similar methods to solve the smaller problems

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9
Q

What is Abstraction?

A

To omit specific details of a problem and focus on the general patterns

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10
Q

What is Algorithm Writing?

A

A series of instructions that explain how to solve a problem

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11
Q

What is a real world example of a algorithm?

A

A recipe, knitting pattern, IKEA dresser assembly

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12
Q

In what terms do computers think in?

A
  1. Math
  2. Logic
  3. 0’s
  4. 1’s
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13
Q

What are the 5 stages of communicating with computers?

A
  1. Human thinking big, complex thoughts
  2. Human learning how to think like a computer (Computational Thinking)
  3. Human learning to translate big, complex thought into computer language like JavaScript
  4. Computer program that translates the message (JavaScript) into 0’s and 1’s
  5. Computer receiving the message of the 0’s and 1’s
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14
Q

What is meant by the word “problem” in computer programming?

A

A challenge or issue that has yet to be solved

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15
Q

What is the core question of Abstraction?

A

What parts of the problem are absolutely necessary for me to solve?

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16
Q

What does Abstraction allow me to do?

A

Focus on only the things that are necessary

17
Q

What is the best way to find if there is a glitch in a solution?

A

Test it out with one thing first to see what happens

18
Q

Breaking down a big problem into smaller problems is known as what?

A

Decomposition

19
Q

When you look for similarities between problems you’re trying to solve, what is that called?

A

Pattern Recognition

20
Q

When you turn everything you’ve learned about the problem you’re solving into a series of repeatable instructions, what’s that?

A

Algorithm Writing

21
Q

What does my computer know?

A

Basically nothing

22
Q

How would we use the first step of Decomposition to tell the computer how to make a peanut butter and jelly sandwich?

A
  1. To list out all of the needed ingredients and tools to make the sandwich.
  2. List out all of the actions needed to make the sandwich.
23
Q

How would we use the second step of Pattern Recognition to find actions that repetitive in the peanut butter and jelly making process?

A

We can see that there is both opening of a jar and spreading an ingredient in both the peanut butter and jelly process

24
Q

Why is finding a pattern in process helpful?

A

Because if I solve a problem for one then I solve it for all with the same pattern.

25
Q

How would we use the third step of Abstraction to find only the necessary details for making a peanut butter and jelly sandwich?

A

Cutting the problem down to basics of only Peanut Butter + Jelly + Bread = Sandwich

26
Q

What is the hardest thing about Abstraction?

A

Leaving out all of the details

27
Q

What can Abstraction help us you out with?

A

It can help you check your assumptions

28
Q

What must you sometimes do in order to solve one small part of a problem?

A

You sometimes have to make assumptions

29
Q

What are some assumptions I may want to make when thinking through Abstract parts of the peanut butter and jelly sandwich problem?

A
  1. The computer knows what a knife is
  2. The computer knows how to open jars
  3. The bread is not in a bag
  4. The bread is sliced

Coding (4 decks)

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